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ETS-Data

ETS-Data is jointly established by Tsinghua University Press and School of Vehicle and Mobility, Tsinghua University, China and is a publicly accessible database, providing indispensable materials for result replications (data, codes, scripts, simulations, experimental designs, etc.). ETS-Data has been indexed by DCI (Data Citation Index) and Google Dataset Search.

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List

  • Published on: 2024-03-05

    Laboratory Experimental Datasets on Route-choice Games

    Hang Qi, Zhengbing He, Ning Jia

    Total 312 participants in approximately equal proportions of males and females were invited to participate the decision-making experiments for the payoffs that were contingent on their performance. The participants were randomly assigned to 17 groups and they were required to make DTD route choices with no mutual communication. The following eight DTD scenarios with the same origin-destination (OD) pair were set.

    • Scenario 1 was the baseline scenario containing a symmetric two-route network.
    • Scenarios 2-5 extended Scenario 1 by using asymmetric two-route networks and different cost functions to investigate subject’s route choice behaviors under different cost feedback.
    • Scenarios 6-7 employed asymmetric networks with three routes to observe more complicated route choice behavior.          •
    Scenario 8 extended the configuration to non-linear cost functions and different group sizes (24 subjects per group in Scenario 8, while 16 in others), which would demonstrate the robustness of the proposed theoretical model.

    Bounded rationalityLaboratory experiment
    DOI: 10.26599/ETSD.2024.9190001
    CSTR: 32009.11.ETSD.2024.9190001
    Asia, China, Tianjin
  • Published on: 2023-12-27

    Multi-Level Objectives Control of AVs at A Saturated Signalized Intersection with Multi-Agent Deep Reinforcement Learning Approach

    Wenfeng Lin

    Code and experimental data for "Multi-Level Objectives Control of AVs at A Saturated Signalized Intersection with Multi-Agent Deep Reinforcement Learning Approach."Codes are written in Python and developed on an open-source tool “Flow”.( https://flow.readthedocs.io/en/latest/index.html)

     

    Mixed trafficAutonomous vehicles
    DOI: 10.26599/ETSD.2023.9190025
    CSTR: 32009.11.ETSD.2023.9190025
    Global
  • Published on: 2023-08-01Updated on: 2023-12-17

    Vehicle and charging scheduling of electric bus fleets: a comprehensive review

    Le Zhang, Yu Han, Jiankun Peng, Yadong Wang

    Purpose — Transit electrification has emerged as an unstoppable force, driven by the considerable environmental benefits if offers. Nevertheless, the adoption of battery electric bus is still impeded by its limited flexibility. The constraint necessitates adjustments to current bus scheduling plans. To this end, this paper aspires to offer a thorough review of articles focused on battery electric bus scheduling.

    Design/methodology/approach — We provide a comprehensive review of 42 papers on electric bus scheduling and related studies, with a focus on the most recent developments and trends in this research domain.

     

    Charging scheduleElectric bus
    DOI: 10.26599/ETSD.2023.9190017
    CSTR: 32009.11.ETSD.2023.9190017.V3
    Asia, China
  • Published on: 2023-11-20

    VTOL sites location considering obstacle clearance during approach and departure

    Yanjun Wang

    This dataset constains data and codes for determining the VTOL sites.

    UamVtol sites
    DOI: 10.26599/ETSD.2023.9190024
    CSTR: 32009.11.ETSD.2023.9190024
    Asia, China, Shenzhen
  • Published on: 2023-10-31

    Empowering highway network: optimal deployment and strategy for dynamic wireless charging lanes

    Lingshu Zhong

    The OD data in this paper are from the real road network data of Guangdong province. The initial SoC in the paper is randomly generated and follows a normal distribution.

    OdSoc
    DOI: 10.26599/ETSD.2023.9190023
    CSTR: 32009.11.ETSD.2023.9190023
    Asia, China, Guangdong
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Journal
Overview

Communications in Transportation Research

Communications in Transportation Research publishes peer-reviewed high-quality research representing important advances of significance to emerging transport systems. The mission is to provide fair, fast, and expert peer review to authors and insightful theories, impactful advances, and interesting discoveries to readers. We welcome submissions of significant and general topics, of inter-disciplinary nature (transport, civil, control, artificial intelligence, social science, psychological science, medical services, etc.), of complex and inter-related system of systems, of strong evidence of data strength, of visionary analysis and forecasts towards the way forward, and of potentially implementable and utilizable policies/practices. It is indexed in Scopus and DOAJ.

Indexed by international databases